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A Double-Stage Genetic Optimization Algorithm for Portfolio Selection

  • Kin Keung Lai
  • Lean Yu
  • Shouyang Wang
  • Chengxiong Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitz’s theory. Through the two-stage genetic optimization process, an optimal portfolio can be determined. Experimental results reveal that the proposed double-stage genetic optimization algorithm for portfolio selection provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio.

Keywords

Genetic Algorithm Root Mean Square Error Portfolio Optimization Portfolio Selection Asset Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kin Keung Lai
    • 1
    • 2
  • Lean Yu
    • 2
    • 3
  • Shouyang Wang
    • 1
    • 3
  • Chengxiong Zhou
    • 3
  1. 1.College of Business AdministrationHunan UniversityChangshaChina
  2. 2.Department of Management SciencesCity University of Hong KongKowloonHong Kong
  3. 3.Institute of Systems ScienceAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina

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